Selective Search

Computer VisionIntroduced 200023 papers

Description

Selective Search is a region proposal algorithm for object detection tasks. It starts by over-segmenting the image based on intensity of the pixels using a graph-based segmentation method by Felzenszwalb and Huttenlocher. Selective Search then takes these oversegments as initial input and performs the following steps

  1. Add all bounding boxes corresponding to segmented parts to the list of regional proposals
  2. Group adjacent segments based on similarity
  3. Go to step 1

At each iteration, larger segments are formed and added to the list of region proposals. Hence we create region proposals from smaller segments to larger segments in a bottom-up approach. This is what we mean by computing “hierarchical” segmentations using Felzenszwalb and Huttenlocher’s oversegments.

Papers Using This Method

A Realistic Protocol for Evaluation of Weakly Supervised Object Localization2024-04-15Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms2022-10-29Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance2022-10-26MICO: Selective Search with Mutual Information Co-training2022-09-09Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning2022-05-09Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning2021-11-26DETReg: Unsupervised Pretraining with Region Priors for Object Detection2021-06-08Aligning Pretraining for Detection via Object-Level Contrastive Learning2021-06-04An Ultra Lightweight CNN for Low Resource Circuit Component Recognition2020-10-01Learning Objectness from Sonar Images for Class-Independent Object Detection2019-07-01You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection2019-06-01RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles2019-05-01Automatic Handgun Detection in X-ray Images using Bag of Words Model with Selective Search2019-03-04Semantic Hierarchical Priors for Intrinsic Image Decomposition2019-02-11Learning Position Evaluation Functions Used in Monte Carlo Softmax Search2019-01-30Deep Multiple Instance Learning for Zero-shot Image Tagging2018-03-16ME R-CNN: Multi-Expert R-CNN for Object Detection2017-04-04Deep Learning the Indus Script2017-02-02A MultiPath Network for Object Detection2016-04-07Diversity in Object Proposals2016-03-14